Field
Embodiments of the present invention generally relate to a system and method to authenticate contact center agents of an enterprise and particularly to a system and method to authenticate contact center agents by a reverse authentication procedure.
Description of Related Art
Contact centers are employed by many enterprises to service inbound and outbound contacts from customers. A primary objective of contact center management is to ultimately maximize contact center performance and profitability. An ongoing challenge in contact center administration is monitoring and optimizing contact center efficiency usage of its available resources. The contact center efficiency is generally measured by metrics such as Service Level Agreement (SLA), Customer Satisfaction (CSAT), and match rate. Contact center resources may include, agents, communication assets (e.g., number of voice trunks, number and bandwidth of video trunks, etc.), computing resources (e.g., a speed, a queue length, a storage space, etc.), and so forth.
Service level is one measurement of the contact center efficiency. Service level is typically determined by dividing the number of contacts accepted within a specified period by the number accepted plus number that were not accepted, but completed in some other way (e.g., abandoned, given busy, canceled, flowed out). Service level definitions may vary from one enterprise to another.
Match rate is another indicator used in measuring the contact center efficiency. Match rate is usually determined by dividing the number of contacts accepted by a primary skill level agent within a period of time by the number of contacts accepted by any agent in a queue over the same period. An agent with a primary skill level is one who typically may handle contacts of a certain nature more effectively and/or efficiently as compared to an agent of lesser skill level. There are other contact center agents who may not be as proficient as the primary skill level agent, and those agents are identified either as skill level agents or backup skill level agents. As can be appreciated, contacts received by a primary skill level agent are typically handled more quickly and accurately or effectively (e.g., higher revenue attained) than a contact received by a secondary or even backup skill level agent. Thus, it is an objective of most contact centers to optimize match rate along with the service level.
In addition to service level and match rate performance measures, contact centers use other Key Performance Indicators (“KPIs”), such as revenue, estimated, actual, or predicted wait time, average speed of answer, throughput, agent utilization, agent performance, agent responsiveness and the like, to calculate performance relative to their Service Level Agreements (“SLAs”). Operational efficiency is achieved when the KPIs are managed near, but not above, SLA threshold levels.
Throughput is a measure of the number of calls/contact requests or work requests that may be processed in a given amount of time. Agent utilization is a measure of how efficiently the agents' time is being used. Customer service level is a measure of the time customers spend waiting for their work to be handled. Company contact center customers wish to provide service to as many requests as possible in a given amount of time, using the least number of agents to do so, and minimizing the wait time for their customers that may increase the Service Level Agreement (SLA) of the contact center. Further, the contact center may also have to maintain the Customer Satisfaction (CSAT) metrics in order to maintain the KPIs of the contact center. For this purpose, agents may have to maintain the quality of services provided to the customers through multimedia (e.g., voice calls, video calls, emails, etc.).
In today's communicatively connected world, the massive growth in information has been the key to the sustainable growth of the human race. To keep this sustainable growth intact, security of the information transferring from one point to another point holds primal importance. These days, hackers continuously try to steal crucial information from various enterprises such as financial institutions, insurance companies, health care service providers, consumer goods companies and so on. The modus operandi of these peoples are simple which is they pose as fake contact center agents on behalf of these enterprises, gather somehow some very basic information about the account holders who are having any kind of accounts at those enterprises and then trick those unsuspecting users to divulge a plurality of crucial personal information about themselves. Most of the times peoples who receive such calls from these fake agents do not have any way to verify the authenticity of the callers.
Further, to reduce costs of operating a contact center on their own, many enterprises outsource the responsibilities of running contact centers with smaller firms. In this way, a plurality of crucial user-related information is shared with many other people, some of whom may have ill intent. Due to this, users may also be hesitant to disclose their crucial personal information, which in turn may hamper the overall functionality of the contact center. In the initial five months of 2014, some of the United Kingdom's (UK's) top financial institutions have reported a combined loss of over £ 21 million due to multiple phishing attacks. Phishing attacks are the most common fraud attack in today's financial world, in which fake people pose as contact center agents of a financial institution, contact unsuspecting users having accounts with those financial institutions and trick those users to divulge their personal information. According to another statistic, for every 2000 phishing attacks, every person on an average loses more than £ 10,000.
Common conventional techniques to detect financial fraud include: verifying the caller by installing calling line identification (CLI); periodically receiving feeds from service providers; installing an app on a smartphone that can detect a bogus caller. Though each of these disclosed techniques may help to differentiate between an original contact center agent and a bogus agent, they are still not fully fraud proof. For example, hackers may easily hide their bogus calling numbers even from CLI or true caller so that they cannot be detected by those techniques. None of the current conventional techniques provide an approach by which a person who is receiving such a call from a bogus agent actually can validate the authenticity of the callers.
Thus, there is a need for a system and method to validate the authenticity of contact center agents to eliminate the risks of fraud.